PROJECT SUMMARY/ABSTRACT Currently, there are nearly 13,000 patients waitlisted for liver transplant, yet only two-thirds will receive a transplant, and in 2018 approximately 2,500 patients died or were removed from the waitlist due to medical deterioration. This shortage of donor livers available for transplant has led to the use of marginal livers ? livers that are higher risk than typical donor livers but may be safely transplantable in carefully selected recipients. These include older donors (>70 years), steatotic livers, and livers procured through donation after cardiac death. Due to their riskiness, these marginal livers are often declined, yet as many as 84% of patients who died on the transplant waitlist declined one or more marginal livers prior to death. In light of this, certain waitlisted candidates might have derived a survival benefit from undergoing transplantation with a marginal organ rather than remaining on the waitlist (i.e. they would have survived longer after a transplant with a marginal liver than they would have survived on the waitlist). Currently, decisions about whether a particular marginal liver is suitable for a particular candidate are based on clinical gestalt or simple subgroup analysis using traditional regression models, which likely do not fully approximate the complex interactions between donor, recipient, and transplant factors. To account for this, we will utilize machine learning (which can incorporate complex, higher-order interactions) to predict whether a specific candidate would derive a survival benefit from undergoing transplantation with a specific marginal liver, and interview transplant candidates and surgeons to understand how best to translate these predictions into an immediately clinically-useful decision aid. To accomplish this, we will leverage Scientific Registry of Transplant Recipient (SRTR) national data (n=293,140) and use a machine technique (random forests) to address the following aims: (1) To predict waitlist survival for waitlisted liver transplant candidates; (2) To predict post-transplant survival for liver transplant recipients of a marginal liver; and (3) To create a decision aid that compares predicted waitlist survival and predicted post-transplant survival for a specific transplant candidates with marginal liver. These aims are highly feasible given our group?s expertise in liver transplantation, analysis of national registry data, and machine learning techniques. We hypothesize that utilizing SRTR and machine learning, we can accurately predict post-transplant survival for a particular candidate with a particular marginal liver, as well as waitlist survival for that same candidate without a liver. We also hypothesize that our decision aid could be utilized in real-time to inform clinical decision-making. If the proposed aims are achieved, our decision aid could be utilized to improve clinical practice by bringing high-quality risk prediction directly to patients and transplant professionals to directly inform the real-time clinical decision of whether a candidate should undergo transplant with a marginal liver.